Project Summary The ability to engineer the microbiome could transform treatment and prevention of diseases from obesity to cancer. The promise of designer microbiomes is largely constrained by lack of understanding of how community composition and function are encoded in the genomes present in a system. The long-term goal is to develop systems-level, metabolically-based approaches to connect genomic data to microbial community function and dynamics. Metabolic mechanisms provide a broadly applicable foundation for understanding and managing microbial systems as metabolic enzymes can be identified from sequence data, and intracellular metabolism drives many of the microbial interactions that generate community behavior. The proposed research will computationally predict and experimentally test the quantitative connection between genome sequence, metabolic mechanisms, and community properties in a microbial community. A model microbial community has been engineered in the laboratory with defined metabolic interactions between Escherichia coli, Salmonella enterica, and Methylobacterium extorquens. Further a computational platform has been developed that uses genome-scale metabolic models to simulate growth and metabolic interactions and community function. These cutting-edge tools will be combined to achieve the following specific aims: Aim 1 ? Identify all metabolic and genetic elements that contribute to growth in a defined community. Genome-scale knockout libraries will be evaluated computationally and empirically. Aim 2 ? Determine how evolution changes community composition and function. High-throughput phenotypic assays and genome sequencing will be used to identify the changes that have evolved in eight replicate communities over 400 generations. Metabolic constraints on evolution will be computationally investigated. Aim 3 ? Test the prevalence of genetic interactions in a microbial community. Genetic interactions within and between genomes will be determined by the frequency with which the effect of a mutation changes in the presence of other mutations. The proposed work will generate the first systems-level data on the genomic basis of microbial community function. It will provide valuable insights into the metabolic and genetic mechanisms underlying dynamics in multi-species systems and the extent to which the effects of genetic changes are context dependent. Finally, the work will enable quantitative prediction of evolutionary trajectories from genome-scale metabolic models. As we strive to engineer microbiomes it is critical to characterize how genomic changes translate to changes in the community. Quantitatively connecting genome sequence to community function is a vital step in the ultimate goal of understanding and rationally managing microbial communities.